Efficient Management Research Articles

Rancang Bangun Sistem Informasi Manajemen Penjualan Kopi Pada Coffee Shop Konamu Menggunakan Sistem Point Of Sale

The purpose of this research is to develop a coffee sales management information system at the KonamuCoffee Shop using a Point of Sale system to increase efficiency in sales management. The method used in thisstudy is the system development life cycle method, starting from system requirements analysis, system design,system implementation, to system testing. In the system requirements analysis stage, assistance is providedfor the problems faced by Konamu Coffee Shop in sales management. Furthermore, a system design is carriedout using the Point of Sale system to facilitate the transaction process and sales management. The result ofthis research is a coffee sales management information system that is integrated with the Point of Sale system.This system can assist Konamu Coffee Shop in managing sales transactions, as well as generating sales andfinancial reports. With this system, it is hoped that the Konamu Coffee Shop can increase efficiency inmanaging coffee sales.

Evaluation of Plant Introduction Lines of Yellow Squash (Cucurbita pepo) for Resistance against Single Infection of Cucurbit Chlorotic Yellows Virus and Cucurbit Leaf Crumple Virus

Whitefly-transmitted viruses have emerged as a major threat to cucurbit production in the United States during the past several decades. Cucurbit chlorotic yellows virus (CCYV), Cucurbit yellow stunting disorder virus (CYSDV), and Cucurbit leaf crumple virus (CuLCrV) are the main arthropod-borne plant viruses in cucurbit crops, including yellow squash (Cucurbita pepo). Symptoms of these viruses include interveinal chlorosis, chlorotic spots, yellowing, and curling of the leaves. The evaluation of specific viruses affecting a plant is challenging because of the prevalence of mixed infections in naturally infected fields. To devise an efficient breeding-based management approach, two PI lines (PI 171625 and PI 171627) were screened in a greenhouse to assess their resistance to individual infections of CCYV and CuLCrV. These lines were compared against a susceptible cultivar Gentry in two separate trials. PI 171627 displayed delayed symptoms, a reduced virus load, and a smaller area under the disease progress curve (AUDPC) compared with PI 171625 and susceptible cultivar Gentry when the plants were infected with CCYV. However, the AUDPC for CuLCrV was the same for both genotypes and the commercial line. Accession PI 171627, which displayed delayed and milder symptoms, could potentially provide a source for resistance against CCYV for breeding program. Future research is needed to comprehend the underlaying mechanism to understand this response.

Modeling Determinants of Profitability in Ethiopia Manufacturing Companies: A Dynamic Panel Model Approach

Background: Profitability is the primary measure of the overall success of company. The analysis of profitability ratios is important for the shareholders, creditors, prospective investors, manufacturers and government alike. Objective: The objective of this study was fitted a dynamic panel regression model that efficiently fit the data and further to identify the determinants contributing significantly to profitability in Ethiopia. Methods: This study was conducted on secondary data of 32 sample manufacturing companies collected from the audited financial statements of large tax payers’ office and national bank of Ethiopia. The study was covered a period of seven years from 2011 to 2017. The panel unit root test of Levin-Lin-Chu tests was made for each variable and applied first difference transformation for the variables that had unit root. A dynamic panel regression model was utilized for data handling technique using generalized method of moments (GMM) estimation. Results: We compared the results when one unit increase in lagged profitability, managerial efficiency, capital intensity, GDP, exchange rate and one ratio decrease in leverage, ceteris paribus, turn out were found to increase the profitability of manufacturing companies by around 0.69, 0.179, 4.52E-06, 3.844, 0.04 and 0.393 ratio, respectively. The model reveled that previous profitability, leverage, capital intensity, managerial efficiency, GDP and exchange rate had a statistically significant (P<0.05) effect on companies’ profitability in Ethiopia. Conclusion: As per our findings, the manufacturing companies should minimize leverage financing from its capitals and should emphasize the management of appropriate financing to increase profitability. Finally, we recommended that policy makers should coming up with better policies on improvement of profitability.

Suspension Based on Magnetic Effect for Vehicles

Abstract: Suspension systems are integral to vehicle performance, playing a critical role in enhancing ride comfort, stability, and handling. By absorbing shocks and vibrations, suspension systems ensure a smoother ride for occupants, improve traction, and maintain optimal tire contact with the road. Effective suspension systems also contribute to overall vehicle safety by optimizing braking performance and manoeuvrability. This research paper explores the benefits of magnetic suspension systems and their applications across various fields. Magnetic suspension offers significant advantages over traditional mechanical systems, including superior stability, quick dynamic responses, and efficient thermal management. Through detailed analysis and comparative studies, this paper highlights the potential of magnetic suspension to revolutionize modern transportation and other industries requiring precise control and stability.

A Machine Learning and Optimization Framework for Efficient Alert Management in a Cybersecurity Operations Center

Cybersecurity operations centers (CSOCs) protect organizations by monitoring network traffic and detecting suspicious activities in the form of alerts. The security response team within CSOCs is responsible for investigating and mitigating alerts. However, an imbalance between alert volume and available analysts creates a backlog, putting the network at risk of exploitation. Recent research has focused on improving the alert-management process by triaging alerts, optimizing analyst scheduling, and reducing analyst workload through systematic discarding of alerts. However, these works overlook the delays caused in alert investigations by several factors, including: (i) false or benign alerts contributing to the backlog; (ii) analysts experiencing cognitive burden from repeatedly reviewing unrelated alerts; and (iii) analysts being assigned to alerts that do not match well with their expertise. We propose a novel framework that considers these factors and utilizes machine learning and mathematical optimization methods to dynamically improve throughput during work shifts. The framework achieves efficiency by automating the identification and removal of a portion of benign alerts, forming clusters of similar alerts, and assigning analysts to alerts with matching attributes. Experiments conducted using real-world CSOC data demonstrate a 60.16% reduction in the alert backlog for an 8-h work shift compared to currently employed approach.

The Natural Landscape as A Strategic Element in Nature Tourism Management

Objective: The aim of this study is to investigate the role of the natural landscape as a strategic element in nature tourism management, in order to understand its conceptual evolution, analyze its relationship with tourism activities and explore the processes of valorization and stereotyping of landscapes in the context of sustainable tourism development. Method: The methodology adopted for the research includes an exhaustive bibliographic search in high-impact databases such as Scopus and Web of Science, complemented with a search in regional databases in Spanish such as Scielo and Redalyc. The selected documents were subjected to a process of critical reading and content analysis to extract key concepts, theories and findings relevant to the study. Results and Discussion: The results revealed that the concept of landscape has evolved from a purely aesthetic and visual perspective to a more holistic understanding that encompasses social, cultural and economic dimensions. The discussion highlights the close relationship between natural landscape and tourism, with landscape serving as a key resource and attraction for nature-based tourism activities. The processes of valorization and stereotyping of landscapes are also explored, emphasizing their influence on tourists' perceptions and destination choice. Research Implications: The findings of this study have implications for sustainable tourism planning and management, especially in areas with significant natural and cultural landscapes. The research emphasizes the need for a multidisciplinary approach to landscape conceptualization and management, as well as the importance of considering landscape as a central element in the development of sustainable tourism products and experiences. Originality/Value: This study contributes to the literature by providing a comprehensive and up-to-date review of the relationship between natural landscape and nature tourism, bridging knowledge from diverse disciplines.

Breaking down the Barriers between the Digital and the Real: Mixed Reality applied to Battery Manufacturing R&D and Training

AbstractIn a scenario in which the manufacturing of high‐performance, safe batteries on an unprecedented large scale is crucial for the energy transition and fight against climate change, research laboratories and cell production industries are facing challenges due to the lack of efficient data management and training tools. In this context, the use of intelligent devices plays an important role on the path towards the optimization of the manufacturing process and the enhancement of the battery performance while reducing production costs. In this Concept, we present an innovative Mixed Reality tool for efficient data collection and training in real‐time in battery research laboratories and battery manufacturing pilot lines, which runs on Microsoft HoloLens 2 glasses. We report a deep analysis on its ergonomic and usability aspects, and describe how we solved the problems found during its development. Thanks to this tool, users can collect data while keeping their hands free and receive advice in real time to design and build batteries with tailored properties. This optimizes data management in battery manufacturing environments. Now, thanks to our Mixed Reality application, users can collect data in the place of work, save this data automatically on a server and exploit it to receive advice and feedback to support their decision‐making and learning of the manufacturing process.

An effectual recommendation model using hybrid learning models for early detection of Alzheimer’s disease

Alzheimer’s disease (AD) is a neurodegenerative disorder that affects millions of individuals worldwide, causing progressive cognitive decline. Early prediction and diagnosis the AD accurately is crucial for effective intervention and treatment. In this study, we propose a comprehensive framework for AD prediction using various techniques, including preprocessing and denoising with Multilayer Perceptron (MLP) and Ant Colony Optimization (ACO), segmentation using U-Net, and classification with Spatial Pyramid Pooling Network (SPPNet). Furthermore, we employ Convolutional Neural Network (CNN) with SPPNet for training and develop a chatbot for recommendation based on MRI data input. The preprocessing and denoising techniques play a vital role in enhancing the quality of the input data. MLP is utilized for preprocessing, where it effectively handles feature extraction and noise reduction. ACO is employed for denoising, optimizing the data to improve the signal-to-noise ratio, and enhancing the overall performance of subsequent stages. For accurate segmentation of brain regions, we employ the U-Net architecture, which has shown remarkable success in medical image segmentation tasks. U-Net effectively identifies the regions of interest, aiding in subsequent classification stages. The classification phase utilizes SPPNet, a deep learning model known for its ability to capture spatial information at multiple scales. SPPNet extracts features from segmented brain regions, enabling robust classification of AD and non-AD cases. To enhance the training process, we employ CNN with SPPNet, leveraging the power of convolutional layers to capture intricate patterns and improve predictive accuracy. The CNN-SPPNet model is trained on a large dataset of MRI scans, enabling it to learn complex representations and make accurate predictions. Hence the proposed work can be integrated with a chatbot that takes MRI data as input and provides recommendations based on the predicted AD probability. Experimental evaluation shows that the combination of preprocessing, denoising, segmentation, and classification offers a comprehensive solution for accurate and efficient AD diagnosis and management.

Anthracycline-Induced Cardiomyopathy in Cancer Survivors: Management and Long-Term Implications.

Recent advancements in personalized treatments, such as anthracycline chemotherapy, coupled with timely diagnoses, have contributed to a decrease in cancer-specific mortality rates and an improvement in cancer prognosis. Anthracyclines, a potent class of antibiotics, are extensively used as anticancer medications to treat a broad spectrum of tumors. Despite these advancements, a considerable number of cancer survivors face increased risks of treatment complications, particularly the cardiotoxic effects of chemotherapeutic drugs like anthracyclines. These effects can range from subclinical manifestations to severe consequences such as irreversible heart failure and death, highlighting the need for effective management of chemotherapy side effects for improved cancer care outcomes. Given the lack of specific treatments, early detection of subclinical cardiac events post-anthracycline therapy and the implementation of preventive strategies are vital. An interdisciplinary approach involving cardiovascular teams is crucial for the prevention and efficient management of anthracycline-induced cardiotoxicity. Various factors, such as age, gender, duration of treatment, and comorbidities, should be considered significant risk factors for developing chemotherapy-related cardiotoxicity. Tools such as electrocardiography, echocardiography, nuclear imaging, magnetic resonance imaging, histopathologic evaluations, and serum biomarkers should be appropriately used for the early detection of anthracycline-related cardiotoxicity. Furthermore, understanding the underlying biological mechanisms is key to developing preventive measures and personalized treatment strategies to mitigate anthracycline-induced cardiotoxicity. Exploring specific cardiotoxic mechanisms and identifying genetic variations can offer fresh perspectives on innovative, personalized treatments. This chapter aims to discuss cardiomyopathy following anthracycline therapy, with a focus on molecular mechanisms, preventive strategies, and emerging treatments.

Secondary diabetes due to different etiologies: Four case reports

BACKGROUND As research on diabetes continues to advance, more complex classifications of this disease have emerged, revealing the existence of special types of diabetes, and many of these patients are prone to misdiagnosis and underdiagnosis, leading to treatment delays and increased health care costs. The purpose of this study was to identify four causes of secondary diabetes. CASE SUMMARY Secondary diabetes can be caused by various factors, some of which are often overlooked. These factors include genetic defects, autoimmune disorders, and diabetes induced by tumours. This paper describes four types of secondary diabetes caused by Williams–Beuren syndrome, Prader–Willi syndrome, pituitary adenoma, and IgG4-related diseases. These cases deviate significantly from the typical progression of the disease due to their low incidence and rarity, often leading to their neglect in clinical practice. In comparison to regular diabetes patients, the four individuals described here exhibited distinct characteristics. Standard hypoglycaemic treatments failed to effectively control the disease. Subsequently, a series of examinations and follow-up history confirmed the diagnosis and underlying cause of diabetes. Upon addressing the primary condition, such as excising a pituitary adenoma, providing glucocorticoid supplementation, and implementing symptomatic treatments, all patients experienced a considerable decrease in blood glucose levels, which were subsequently maintained within a stable range. Furthermore, other accompanying symptoms improved. CONCLUSION Rare diseases causing secondary diabetes are often not considered in the diagnosis of diabetes. Therefore, it is crucial to conduct genetic tests, antibody detection and other appropriate diagnostic measures when necessary to facilitate early diagnosis and intervention through proactive and efficient management of the underlying condition, ultimately improving patient outcomes.

Prevalence, under-reporting, and epidemiological surveillance of COVID-19 in the Araguaína City of Brazil.

Asymptomatic and underreported individuals remain a source of coronafig disease 2019 (COVID-19) transmission to others. Data on the prevalence and epidemiological factors influencing transmission are fundamental for establishing control measures, especially in vulnerable regions such as the Amazon. This study aimed to determine the point prevalence and active infection of COVID-19 among the population in Araguaína, a Brazilian city located in the Amazon region, analyzed the socioeconomic and behavioral variables of a statistically representative sample of this population using an epidemiological survey, and identify the viral genomic diversity in the region. During the sixth epidemiological week of 2021 (February 8 to 12), samples of 497 inhabitants of the municipality asymptomatic for respiratory syndromes underwent reverse transcription-quantitative polymerase chain reaction and serological tests (immunoglobulin M and immunoglobulin G). A questionnaire collated data on socioeconomic factors, prevention measures, and health status history. The active infection rate was 6.2%, and the prevalence was 13.5% of the study population. Active infection cases were under-reported; each reported positive case represented 14-28 under-reported cases. Lineages P.2, P.1, and B.1.1 were detected. Working from home was a protective factor against the infection, and clinical signs of fever, dry cough, and loss of taste or smell were associated with testing positive (p <0.05). A descriptive analysis of the indicators revealed that the entire population was susceptible to the disease. Intensified vaccination strategies are required regardless of socioeconomic factors, health conditions, and preventive measures. Implementation of objective, comprehensive, and efficient management tools to minimize the spread of COVID-19 in this municipality can serve as a model for other regions of Brazil.

Revolutionizing the Rental Services in Siargao Island: Basis for Developing an Online Vehicle Rental Management System

The research paper addresses the inefficiencies faced by tourists and local rental service providers in the vehicle rental sector of Siargao Islandin the Philippines. Despite the thriving tourism industry in the island, the current manual processes for vehicle rentals are fraught with challenges such as unreliable service, booking difficulties, and inefficient fleet management. This study aims bridge the gap between these traditional practices and modern technological solutions by proposing an online vehicle rental management system designed to streamline operations and enhance customer experience. Using a mixed-methods approach, the exploration integrates qualitative insights from ethnographic studies and interviews while quantitative data from the surveys targeting tourists, residents, and rental service providers. This methodology enables a thorough understanding of the operational challenges and user needs within the rental ecosystem. The findings revealed significant inadequacies in manual booking thus highlighting a strong demand for an automated and user-friendly online platform. The proposed system incorporates advanced system software to automate bookings, optimize fleet management, and provide real-time updates and notifications by enhancing operational efficiency, reducing errors, and improving service reliability. Additionally, it seeks to empower local rental businesses through provision with tools to manage their operations more effectively and compete in the digital age. The research underscores the potential of the proposed system to revolutionize rental services in Siargao Island by improving accessibility, convenience, and customer satisfaction. The future implementation of this online platform is expected to contribute significantly to the sustainable growth of Siargao's tourism industry, ultimately benefiting tourists, local businesses, and the broader community

Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis

Accurate traffic simulation models play a crucial role in developing intelligent transport systems that offer timely traffic information to users and efficient traffic management. However, calibrating these models to represent real-world traffic conditions accurately poses a significant challenge due to the dynamic nature of traffic flow and the limitations of traditional calibration methods. This article introduces a machine learning-based approach to calibrate macroscopic traffic simulation models using real-time traffic video stream data. The proposed method for creating and calibrating a traffic simulation model has significantly improved the statistical correspondence between the generated vehicle characteristics and real data about cars on the simulated road section. The correspondence has increased from 37% to 73%. Machine learning models trained on generated data and tested on real data show improved accuracy rates. Mean absolute error, mean square error, and mean absolute percentage error decreased by more than two orders of magnitude. The coefficient of determination has also increased, approaching 1. This method eliminates the need to deploy wireless sensor networks, which can reduce the cost of implementing intelligent transport systems.

Study of Heat Transfer Characteristics of Heat Pipe Using Water as a Working Fluid

Heat pipes are efficient and versatile thermal management devices widely used in various applications to transfer heat. This study focuses on investigating the heat transfer characteristics of a heat pipe employing de-ionized water as the working fluid. The experiment involved analyzing the thermal performance, heat transport capacity, and operational limits of the heat pipe. The aim of the experiment is to assess the heat pipe's performance under different operating conditions, such as varying heat loads. The study aimed to understand the heat transfer limitations and advantages of water-based heat pipes, which can have practical implications for numerous applications, such as electronics cooling, solar thermal systems, and space technology

DC Power Boosting Circuit for Freestanding‐Sliding Triboelectric Nanogenerators with High Intrinsic Impedance and Multi‐Harmonic Output

AbstractThis study presents a freestanding sliding triboelectric nanogenerator (FS‐TENG) designed for low‐frequency motions (below 2 Hz). However, for practical applications, an efficient power management strategy is required to handle the harmonic outputs of the FS‐TENG. The high‐frequency signal is not sustainable for powering low‐power electronic applications. To address the issue, a novel direct current power supply circuit (DPS) is proposed that utilizes a double charge circuit (DCC) and a comb filtering circuit (CFC) to efficiently harness harmonic sources from the FS‐TENG. The direct power supply (DPS) outperforms traditional converters by reducing the impedance of the FS‐TENG and collecting the target harmonic sources, which facilitates a continuous power supply to the load. The results demonstrate that the DPS is capable of providing a continuous DC voltage of 2.2 V to a 10 MΩ load with minimal ripple (0.039%) at a low operating frequency of 0.625 Hz. Additionally, the practical application of a self‐powered temperature sensor is demonstrated to highlight the potential of FS‐TENG and DPS for low‐frequency energy harvesting solutions in real‐world scenarios.

Incorporating demand constraints into piecewise frontier models of public service provision

Evaluating the performance of public service providers is often complicated by the fact that they must choose input levels before demands for their services are known. We consider an even more complicated situation in which service providers have no opportunity to directly influence demands. This means that their predetermined inputs may be more than what is required to meet realised demands. In such cases, conventional measures of revenue efficiency will generally mis-classify rational and efficient managers as inefficient. We develop a more appropriate measure of revenue efficiency that accounts for exogenously-determined demands. We explain how data envelopment analysis (DEA) methods can be used to estimate our measure. The methodology is applied to hospital and health service networks in Queensland (Australia). We find that most of these networks were able to maximise the revenue they could obtain from their predetermined inputs.

Strategic Management of Financial and Economic Security: Modern Challenges and Threats in a Hyperdynamic External Environment

The main purpose of the article is to increase the efficiency of strategic management of the financial and economic security of a modern enterprise in the face of new challenges and threats. The object of the study is the system of financial and economic security of a modern company. The scientific task is to assess modern surpluses and threats in the system of ensuring the financial and economic security of an enterprise. The research methodology involves the use of modern methods of system analysis, pairwise comparison and hierarchical analysis to assess challenges and threats. As a result, a matrix of the most significant challenges and threats in the system of ensuring the financial and economic security of a modern enterprise was built. The innovativeness of the results obtained is revealed through the proposed methodological approach to assessing and streamlining challenges and threats in the system of ensuring the financial and economic security of a modern enterprise.

Effectiveness of Halal Traceability and Self-Declared Certification on Indonesian MSMEs Performance

This study aims to examine in depth the effectiveness of halal tracea-bility in Indonesia and identify the impact of self-declared halal certifi-cation on the performance of small and medium-sized enterprises (SMEs) in the country. The research method used is qualitative, which is reinforced by statistical analysis through survey data from the Popu-lix application as well as a comprehensive literature review. Data anal-ysis was conducted through various techniques, including regression tests, correlation tests, chi-square tests, and content analysis. The re-search findings confirm that the effectiveness of the halal tracking sys-tem significantly contributes to improving the resilience and competi-tiveness of MSMEs. Moreover, self-declared halal certification has been proven to increase consumer confidence in halal products and their integrity. This research makes an important contribution by high-lighting the urgency of integrating efficient halal supply chain man-agement practices, including reliable tracking systems and credible cer-tification. Furthermore, this research underscores the necessity of en-hancing consumer awareness of halal products, thereby facilitating in-formed purchasing decisions and fostering the growth of a more resili-ent halal ecosystem in Indonesia.

Optimization of battery/ultra‐capacitor hybrid energy storage system for frequency response support in low‐inertia microgrid

AbstractModern power system networks are under statutory obligations to integrate renewable energy sources (RES). The primary reason is to meet ever‐increasing energy demand and also to curtail environmental pollution by greenhouse gases. However, the higher penetration of RES has the tendency of reducing inertia of overall power system network. Consequently, frequency stability is affected and deviates beyond allowable permissible limits leading to power blackouts, load shedding, and even total system failure. To address the issues associated with reduced inertia, an optimal control of hybrid energy storage system (HESS) has been proposed. HESS is basically a combination of battery and ultracapacitor, where ultracapacitor addresses rapidly varying power component by mimicking inertia while the battery compensates long‐term power variations. Thus, the HESS is effectively controlled to compensate the loss of inertia by regulating its energy flow. For the purpose of improved efficiency and better power management of the HESS, an improvised particle swarm optimization (MPSO)‐based virtual inertia control design has been proposed. The proposed MPSO is utilized to tune the gains of bidirectional dc–dc converter in such a way that improves frequency nadir with faster response to transient disturbances. This proposed method is simulated in MATLAB and its merits are validated in real time using hardware in loop. On analysing of the results, it can be observed that frequency nadir is improved by 48.96% with significant reduction in rate of change of frequency in comparison to conventional particle swarm optimization.

Soqia-Advice: A Web-GIS Advisory Platform for Efficient Irrigation in Arboriculture

The determination of water requirements for crops holds a crucial role in optimizing irrigation and enhancing agricultural productivity. However, identifying these needs remains a significant challenge due to the variety of factors influencing this decision, such as meteorological conditions, soil structure, and the phenological stages of each crop. In this study, we propose the design and development of a dedicated web-based irrigation advisory platform for arboriculture named ‘Soqia-Advice’. This platform will provide services to farmers, advisors, and decision-makers. The proposed methodology is based on four main steps: (1) need assessments; (2) definition of functionalities to fulfill these needs; (3) design of the overall architecture and the conceptual data model; and (4) implementation of key features of the module dedicated to farmers. The prototype of the “Farmer” module was tested on a farm in Azrou city, Morocco, as a case study. Seven-day weather forecasts were seamlessly integrated using the Weatherbit API. Additionally, the irrigation schedule was accurately displayed, ensuring efficient water management. Functionality tests were conducted on each menu to ensure the seamless and reliable operation of all planned features. The results were rigorously assessed to ensure that each feature aligned with the identified needs.